The research for this thesis was carried out at the HOMA Software B.V. in Enschede.

Abstract

Micro Combined Heat and Power (micro-CHP) is a new concept to produce both heat and electricity in houses. It replaces a conventional condensing boiler and can be used for both central heating and hot tap water. The difference with a condensing boiler is that part of the energy is used to drive an electricity generator. The produced electricity can be used inside the house, but can also be deliverd to the electricity grid to be consumed by others in the neighbourhood. The advantage of a micro-CHP is that it can produce electricity more efficiently than a large power plant. A micro-CHP appliance only produces electricity if there is a heat demand and not the other way around. When a micro-CHP appliance is combined with a heat buffer, the heat production and electricity consumption can be decoupled. Then it is possible to produce electricity on more beneficial periods within the limits of the heat store. In the near future many micro-CHP appliances will be installed in homes and small businesses. A large fleet of micro-CHP appliances can be combined to serve as a Virtual Power Plant (VPP).

The HOMA Micro-CHP Management System (HMMS) enables the user to monitor and control a large fleet of micro-CHP appliances located in private homes. The HMMS consists of embedded computers that are placed at the micro-CHP appliances and that can communicate with a central server. In this thesis the monitoring part of the HMMS is used to collect the necessary data for anomaly recognition.

Bad performances and total breakdown of the micro-CHP appliances should be avoided as much as possible. The cause of decreasing performance is in many cases mechanical failure and mechanical failures are hard to detect. Therefore, in this thesis the goal is to detect and recognize mechanical failures of micro-CHP appliances as soon as possible. Early detection of anomalies improves the comfort for the residents and increases the energy efficiency of the appliance. The VVP also benefits from the monitoring because it will lead to a larger operational fleet.

During this thesis an adaptive recognition method is developed that can run on the embedded computer of the HMMS. The developed adaptive cognition method is based on a PID-controller. Normally, a PID-controller influences a process to correct the different between a setpoint and the output of the process. In this case the process itself is not influenced. The coorection signals are not used as input for the process to direct the output to the setpoint. The correction signals are only used to correct the output of the process and as a measure for the deviation of the process from the setpoint. The output of the process are the measurements of the real run and the setpoint is set on the expected values that are described in the normal behaviour. The amount of correction that is needed is used to judge whether an anomaly is present.

The developed adaptive recognition method is able to dtect and recognize anomalies. The detection of anomalies works well, the anomalies from both the test cases and the real run can be detected. Recognition of anomalies is also possible but this is only tested with a limited amount of test data and needs more research.